# Osteosarcoma Histopathology Eval

> Evaluates deep learning models on classifying osteosarcoma histopathology images into non-tumor, non-viable tumor, viable tumor, and non-viable ratio categories without prior segmentation. Probes the model's ability to capture local texture and global spatial patterns for medical image classification. Use when the user wants to benchmark on TCIA Osteosarcoma, or asks about evaluating this task. Reports accuracy.

- Skill: `qhjqhj00/osteosarcoma-histopathology-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/osteosarcoma-histopathology-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/osteosarcoma-histopathology-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/osteosarcoma-histopathology-eval

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# osteosarcoma-histopathology-eval

> Advanced Hybrid Deep Learning Model for Enhanced Classification of Osteosarcoma Histopathology Images — Borji et al. (2024) (arXiv:2411.00832, 2024)

## What this evaluates

Evaluates deep learning models on classifying osteosarcoma histopathology images into non-tumor, non-viable tumor, viable tumor, and non-viable ratio categories without prior segmentation. Probes the model's ability to capture local texture and global spatial patterns for medical image classification.

## Datasets

- **TCIA Osteosarcoma** — total ?; splits: test (-1), val (-1)

## Metrics

- `accuracy` **(primary)** — range: [0, 1]
  - Proportion of correctly predicted labels among all predictions.
- `precision` — range: [0, 1]
  - Ratio of true positive predictions to the total number of positive predictions.
- `recall` — range: [0, 1]
  - Ratio of true positive predictions to the total number of actual positives.
- `F1-score` — range: [0, 1]
  - Harmonic mean of precision and recall.

## Input / output format

**Input**: H&E-stained histopathology image slides of osteosarcoma tissue.

**Output**: Class label: binary (VT vs NT), ternary (VT vs NVT vs NT), or quaternary (VT vs NVT vs NT vs NVR).

## Scoring recipe

```python
def compute_metrics(y_true, y_pred):
    accuracy = (y_true == y_pred).mean()
    precision = precision_score(y_true, y_pred, average='macro')
    recall = recall_score(y_true, y_pred, average='macro')
    f1 = f1_score(y_true, y_pred, average='macro')
    return accuracy, precision, recall, f1
```

## Common pitfalls

- The dataset does not require prior segmentation, unlike many prior works that used segmentation techniques.
- Performance varies significantly across classification complexities (2-class vs 4-class), with CNNs dropping in the 4-class task while ViT maintains stability.
- Validation and test accuracies are reported separately; readers must distinguish between them when comparing results.

## Evidence (verbatim from paper)

> Among the tested models, the CNN + ViT hybrid model demonstrated the best performance across all tasks. In the four-class classification, which is the most challenging task, the hybrid model again outperformed all others, with 99.08% test accuracy and 99.70% validation accuracy.

## Citation

```bibtex
@misc{borji2024advanced,
  title={Advanced Hybrid Deep Learning Model for Enhanced Classification of Osteosarcoma Histopathology Images},
  author={Borji et al. (2024)},
  year={2024},
  note={arXiv:2411.00832}
}
```

- arXiv: 2411.00832